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Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China

Author

Listed:
  • Yilei Tao

    (College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China)

  • Ying Wang

    (College of Architecture and Engineering, Zhengzhou University of Industrial Technology, Zhengzhou 450002, China)

  • Xinyu Wang

    (Doctoral School of Landscape Architecture and Landscape Ecology, Technology, Hungarian University of Agriculture and Life Sciences, 1114 Budapest, Hungary)

  • Guohang Tian

    (College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China
    Henan Provincial Joint International Research Laboratory of Landscape Architecture, Henan Provincial Department of Science and Technology, Zhengzhou 450002, China)

  • Shumei Zhang

    (College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China
    Henan Provincial Joint International Research Laboratory of Landscape Architecture, Henan Provincial Department of Science and Technology, Zhengzhou 450002, China)

Abstract

Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.

Suggested Citation

  • Yilei Tao & Ying Wang & Xinyu Wang & Guohang Tian & Shumei Zhang, 2022. "Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China," Land, MDPI, vol. 11(3), pages 1-19, March.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:400-:d:767209
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    References listed on IDEAS

    as
    1. Shumei Zhang & Wenshi Zhang & Ying Wang & Xiaoyu Zhao & Peihao Song & Guohang Tian & Audrey L. Mayer, 2020. "Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    2. Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    3. Yu Ye & Wei Zeng & Qiaomu Shen & Xiaohu Zhang & Yi Lu, 2019. "The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images," Environment and Planning B, , vol. 46(8), pages 1439-1457, October.
    4. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    5. Marleau Donais, Francis & Abi-Zeid, Irène & Waygood, E. Owen D. & Lavoie, Roxane, 2022. "Municipal decision-making for sustainable transportation: Towards improving current practices for street rejuvenation in Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 152-170.
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    Cited by:

    1. Xiaowen Zhou & Hongwei Li & Huili Zhang & Rongrong Zhang & Huan Li, 2022. "A Study on the Cognition of Urban Spatial Image at Community Scale: A Case Study of Jinghu Community in Zhengzhou City," Land, MDPI, vol. 11(10), pages 1-23, September.

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